﻿{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# K均值（K-Means）\n",
    "\n",
    "---\n",
    "\n",
    "### K均值算法是什么？\n",
    "\n",
    "K均值是一种 **聚类（Clustering）** 算法。\n",
    "它的目标是：\n",
    "👉 **把数据分成 K 个组（簇）**，让同一组里的数据尽量相似，不同组之间的数据尽量不同。\n",
    "\n",
    "比如，把一堆水果根据颜色和大小分成 3 类：红苹果一堆、黄香蕉一堆、绿西瓜一堆。\n",
    "\n",
    "---\n",
    "\n",
    "### K均值的基本流程\n",
    "\n",
    "1. **确定 K 值**（想分成几类？比如 3 类）\n",
    "2. **随机选 K 个数据点**作为**初始中心（质心）**\n",
    "3. **分配数据点**：\n",
    "   把每个点分配给**离它最近的中心**。\n",
    "4. **更新中心**：\n",
    "   对每个组，重新计算**组内所有点的平均位置**，作为新的中心。\n",
    "5. **重复**步骤 3 和 4，直到：\n",
    "   - 中心不怎么变化了，或者\n",
    "   - 达到最大循环次数\n",
    "\n",
    "---\n",
    "\n",
    "### 用一句话总结\n",
    "> K均值就是不停地**分类 → 重新计算中心 → 再分类**，直到收敛。\n",
    "\n",
    "---\n",
    "\n",
    "### 一个小例子\n",
    "\n",
    "假设有5个点：\n",
    "`(1,1)，(2,1)，(4,5)，(5,4)，(5,5)`，想分成2类（K=2）。\n",
    "\n",
    "- 随机选 `(1,1)` 和 `(5,5)` 作为初始中心。\n",
    "- 计算每个点离哪个中心近，分组。\n",
    "- 分完组后，算每组的中心，比如 `(1.5, 1)` 和 `(4.67, 4.67)`。\n",
    "- 用新的中心重新分组。\n",
    "- 重复，直到中心不变。\n",
    "\n",
    "最后，就把近的点分到一起了！\n",
    "\n",
    "---\n",
    "\n",
    "### 小注意事项\n",
    "\n",
    "- **K值怎么选？**\n",
    "  通常用经验，或者方法比如 \"肘部法则（Elbow Method）\"。\n",
    "- **初始中心的选择**会影响结果，所以有更好的选法，比如 **K-Means++**。\n",
    "- K均值只适合**凸形分布**，不太适合像圆环那样复杂的结构。"
   ],
   "id": "10db6193e16b0c91"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-30T16:04:58.082815Z",
     "start_time": "2025-04-30T16:04:56.255186Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# 1. 加载 Iris 数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target  # 真实标签（这里只是用来对比）\n",
    "\n",
    "# 2. 创建 KMeans 模型，设定分成 3 类\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "kmeans.fit(X)\n",
    "\n",
    "# 3. 聚类的结果\n",
    "predicted_labels = kmeans.labels_\n",
    "print(\"预测的类别标签:\", predicted_labels)\n",
    "\n",
    "# 4. 把高维数据用 PCA 降到 2 维，方便画图\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "# 5. 可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# 用聚类的标签画\n",
    "for i in range(3):\n",
    "    plt.scatter(X_pca[predicted_labels == i, 0],\n",
    "                X_pca[predicted_labels == i, 1],\n",
    "                label=f'Cluster {i}')\n",
    "\n",
    "# 画出聚类中心\n",
    "centers_pca = pca.transform(kmeans.cluster_centers_)\n",
    "plt.scatter(centers_pca[:, 0], centers_pca[:, 1], c='black', marker='x', s=200, label='Centers')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('K-Means Clustering on Iris Dataset')\n",
    "plt.xlabel('PCA Component 1')\n",
    "plt.ylabel('PCA Component 2')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ],
   "id": "194def7687c3428c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测的类别标签: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 0 0 0 0 2 0 0 0 0\n",
      " 0 0 2 2 0 0 0 0 2 0 2 0 2 0 0 2 2 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2 0 0 0 2 0\n",
      " 0 2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 1
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
